Best of Baeldung — April 2025

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   Baeldung·1y

   Thread per Connection vs Thread per Request

   This post compares two server threading models: thread-per-connection and thread-per-request. It defines the terms 'connection' and 'request', discusses their implementation in Java web servers, and highlights the advantages and disadvantages of each model. Thread-per-connection involves a dedicated thread for each client connection, while thread-per-request uses a new thread for each request, regardless of connection status. The choice between these models depends on factors like scalability, context switching, and expected traffic patterns.

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2. 2
   Article

   Baeldung·1y

   Introduction to Apache Kylin

   Apache Kylin is an open-source OLAP engine designed for sub-second query performance on massive datasets. Initially developed by eBay and later managed by the Apache Software Foundation, it excels in handling high concurrency and integrates seamlessly with Hadoop and data lake platforms. Key features include multidimensional modeling, optimized indexing, and support for both batch and streaming data sources. The platform can be easily explored using Docker, allowing for straightforward setup, model creation, and CUBE building via SQL and REST API.

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3. 3
   Article

   Baeldung·1y

   How to Run a Java Program in the Background

   Learn various methods to run a Java application as a background process, keep it running after terminal closure, and monitor it using logs. Explore the use of '&' operator, nohup, systemd services, screen, and tmux for effective process management.

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4. 4
   Article

   Baeldung·1y

   Monitor a Java Application With New Relic

   Learn how to monitor a Java application using New Relic, an observability platform providing real-time insights into application performance. The tutorial covers setting up a Spring Boot application, configuring the New Relic Java agent, and viewing application metrics on the New Relic dashboard to optimize performance and ensure reliability.

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5. 5
   Article

   Baeldung·1y

   Extracting Structured Data From Images Using Spring AI

   Learn how to extract structured data from images using the OpenAI chat model with Spring AI. The tutorial provides step-by-step instructions to create a web service that accepts image uploads, analyzes the images using OpenAI, and returns the structured data in JSON format. Key components include configuring Maven dependencies, setting up Spring Boot configurations, and defining POJO classes for structured outputs.

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6. 6
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   Baeldung·1y

   Building an AI Chatbot in Java With Langchain4j and MongoDB Atlas

   This guide provides a detailed walkthrough on building an AI chatbot in Java using Langchain4j and MongoDB Atlas. The chatbot can perform similarity searches using vector embeddings to understand user queries better. Dependencies, configuration, document loading flow, and chatbot flow are covered. It also includes setting up the necessary components, creating API endpoints, and testing the chatbot functionality.

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